



import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a      :  Optional[int]			     =			logging.get_logger(__name__)

a      :  int			     =			{
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}







class 				_a				(   _lowerCAmelCase					):
			A      		=	'''conditional_detr'''
			A      		=	['''past_key_values''']
			A      		=	{
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			}

			def __init__(self,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=300,   SCREAMING_SNAKE_CASE_=6,   SCREAMING_SNAKE_CASE_=2048,   SCREAMING_SNAKE_CASE_=8,   SCREAMING_SNAKE_CASE_=6,   SCREAMING_SNAKE_CASE_=2048,   SCREAMING_SNAKE_CASE_=8,   SCREAMING_SNAKE_CASE_=0.0,   SCREAMING_SNAKE_CASE_=0.0,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_="relu",   SCREAMING_SNAKE_CASE_=256,   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=0.0,   SCREAMING_SNAKE_CASE_=0.0,   SCREAMING_SNAKE_CASE_=0.0_2,   SCREAMING_SNAKE_CASE_=1.0,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_="sine",   SCREAMING_SNAKE_CASE_="resnet50",   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=5,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=1,   SCREAMING_SNAKE_CASE_=1,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=5,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=0.2_5,   **SCREAMING_SNAKE_CASE_,   )       ->   Dict:
						if backbone_config is not None and use_timm_backbone:
									raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""					)

						if not use_timm_backbone:
									if backbone_config is None:
												logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""					)
												UpperCAmelCase_:		Any							      =     CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]					)
									elif isinstance(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					):
												UpperCAmelCase_:		Optional[int]							      =     backbone_config.get("""model_type"""					)
												UpperCAmelCase_:		Dict							      =     CONFIG_MAPPING[backbone_model_type]
												UpperCAmelCase_:		str							      =     config_class.from_dict(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Any							      =     use_timm_backbone
						UpperCAmelCase_:		Optional[Any]							      =     backbone_config
						UpperCAmelCase_:		Dict							      =     num_channels
						UpperCAmelCase_:		str							      =     num_queries
						UpperCAmelCase_:		Optional[Any]							      =     d_model
						UpperCAmelCase_:		Any							      =     encoder_ffn_dim
						UpperCAmelCase_:		Any							      =     encoder_layers
						UpperCAmelCase_:		Optional[Any]							      =     encoder_attention_heads
						UpperCAmelCase_:		str							      =     decoder_ffn_dim
						UpperCAmelCase_:		str							      =     decoder_layers
						UpperCAmelCase_:		Tuple							      =     decoder_attention_heads
						UpperCAmelCase_:		Tuple							      =     dropout
						UpperCAmelCase_:		List[str]							      =     attention_dropout
						UpperCAmelCase_:		int							      =     activation_dropout
						UpperCAmelCase_:		List[str]							      =     activation_function
						UpperCAmelCase_:		List[Any]							      =     init_std
						UpperCAmelCase_:		Optional[Any]							      =     init_xavier_std
						UpperCAmelCase_:		Any							      =     encoder_layerdrop
						UpperCAmelCase_:		List[str]							      =     decoder_layerdrop
						UpperCAmelCase_:		Tuple							      =     encoder_layers
						UpperCAmelCase_:		Union[str, Any]							      =     auxiliary_loss
						UpperCAmelCase_:		Union[str, Any]							      =     position_embedding_type
						UpperCAmelCase_:		Optional[Any]							      =     backbone
						UpperCAmelCase_:		str							      =     use_pretrained_backbone
						UpperCAmelCase_:		List[Any]							      =     dilation
						# Hungarian matcher
						UpperCAmelCase_:		Union[str, Any]							      =     class_cost
						UpperCAmelCase_:		int							      =     bbox_cost
						UpperCAmelCase_:		List[Any]							      =     giou_cost
						# Loss coefficients
						UpperCAmelCase_:		str							      =     mask_loss_coefficient
						UpperCAmelCase_:		List[str]							      =     dice_loss_coefficient
						UpperCAmelCase_:		Any							      =     cls_loss_coefficient
						UpperCAmelCase_:		List[str]							      =     bbox_loss_coefficient
						UpperCAmelCase_:		Any							      =     giou_loss_coefficient
						UpperCAmelCase_:		Tuple							      =     focal_alpha
						super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			@property
			def      __snake_case       (self					)       ->   int:
						return self.encoder_attention_heads

			@property
			def      __snake_case       (self					)       ->   int:
						return self.d_model

			def      __snake_case       (self					)       ->   int:
						UpperCAmelCase_:		List[Any]							      =     copy.deepcopy(self.__dict__					)
						if self.backbone_config is not None:
									UpperCAmelCase_:		Tuple							      =     self.backbone_config.to_dict()
						UpperCAmelCase_:		str							      =     self.__class__.model_type
						return output







class 				_a				(   _lowerCAmelCase					):
			A      		=	version.parse('''1.11'''					)

			@property
			def      __snake_case       (self					)       ->   Mapping[str, Mapping[int, str]]:
						return OrderedDict(
						    [
						        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
						        ("""pixel_mask""", {0: """batch"""}),
						    ]					)

			@property
			def      __snake_case       (self					)       ->   float:
						return 1E-5

			@property
			def      __snake_case       (self					)       ->   int:
						return 12











#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist







def  lowerCAmelCase_	(*lowerCAmelCase__:		Dict  ):





			"""simple docstring"""


			with open(lowerCAmelCase__		,						"""r"""  ) as fh:
						fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_EX  )
						try:
									print(*lowerCAmelCase__  )
						finally:
									fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_UN  )


a      :  Tuple			     =			int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
a      :  Union[str, Any]			     =			torch.device('cuda', local_rank)
a      :  Any			     =			socket.gethostname()

a      :  Optional[Any]			     =			F'''[{hostname}-{local_rank}]'''

try:
	# test distributed
	dist.init_process_group('nccl')
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	a      :  Optional[Any]			     =			dist.get_rank()
	a      :  List[Any]			     =			dist.get_world_size()

	printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')

	dist.barrier()
	if rank == 0:
		printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
	printflock(F'''{gpu} is broken''')
	raise






